About the team
Networking brings together innovative ideas and technologies from network architecture, software defined networking (SDN), network virtualization, switch software and hardware co-design, and high-speed networking, to create hyper-scale data-center networking solutions that power several of the most popular apps of the world such as Douyin and TikTok which serve hundreds of millions of users around the globe.
Network Observation team is committed to building a world-leading hyperscale data center network infrastructure that supports hundreds of millions of users' real-time access and explosive growth of massive data volumes.
We believe that the next generation of network operations will be fundamentally powered by artificial intelligence technologies, particularly Large Language Models (LLMs).
We are seeking a passionate development engineer who combines deep networking expertise with innovative AIOps capabilities to join us in defining and building "autonomous" data center networks.
Together, we will transform network operations from a reactive "firefighting" mode into a proactive, data-driven intelligent ecosystem with predictive and self-healing capabilities.
Responsibilities:
As a core member of our team, you will collaborate closely with our NetOps, SRE, and platform engineering teams to tackle the complexities of one of the world's largest data center networks.
You will design and implement a closed-loop AIOps for NetWork platform, covering:
- Build a Panoramic Network Observability Platform: Develop a streaming telemetry data pipeline for both physical and virtual networks, integrating multi-source data from gNMI, Netconf, IPFIX/NetFlow, and
SNMP to provide a high-quality, real-time data foundation for AIOps.
- Develop an Intelligent Diagnostics and Root Cause Analysis System: Apply machine learning and deep learning algorithms to perform anomaly detection, correlation analysis, and intelligent noise reduction on massive volumes of network metrics, logs, and events.
Swiftly pinpoint root causes of failures across the entire stack, from optical transceivers and switch hardware to protocol adjacencies and application traffic.
- Explore Innovative Applications of LLMs and Agents: - Intelligent Operations Assistant: Build a conversational chatbot powered by Retrieval-Augmented Generation (RAG) that understands natural language queries, automatically queries knowledge bases and monitoring data, and provides precise troubleshooting guidance and network status reports.
- Automated Remediation and Smart Runbooks: Train operational Agents to safely and controllably invoke network change tools and APIs.
Empower them to autonomously generate, recommend, or even execute remediation plans and emergency runbooks based on their understanding of failure scenarios.
- Establish Capacity and Risk Prediction Capabilities: Forecast network capacity bottlenecks, high-risk links, and "sub-healthy" devices based on historical data and business growth models, enabling proactive scaling and preventative maintenance.
- Forge a Rock-Solid Engineering System: Adhere to engineering best practices to design and develop a highly available and scalable AIOps platform.
Guarantee the stability and performance of the entire pipeline, from data collection and model training to online inference and automated closed-loop actions.